TY - JOUR AU - Rill, R.A. PY - 2020/04/30 TI - Intuitive Estimation of Speed using Motion and Monocular Depth Information JF - Studia Universitatis BabeČ™-Bolyai Informatica; Vol 65 No 1 (2020)DO - 10.24193/subbi.2020.1.03 KW - N2 - Advances in deep learning make monocular vision approaches attractive for the autonomous driving domain. This work investigates a method for estimating the speed of the ego-vehicle using state-of-the-art deep neural network based optical flow and single-view depth prediction models. Adopting a straightforward intuitive approach and approximating a single scale factor, several application schemes of the deep networks are evaluated and meaningful conclusions are formulated, such as: combining depth information with optical flow improves speed estimation accuracy as opposed to using optical flow alone; the quality of the deep neural network results influences speed estimation performance; using the depth and optical flow data from smaller crops of wide images degrades performance. With these observations in mind, a RMSE of less than 1 m/s for ego-speed estimation was achieved on the KITTI benchmark using monocular images as input. Limitations and possible future directions are discussed as well. UR - https://www.cs.ubbcluj.ro/~studia-i/journal/journal/article/view/49